EP 2646911 B1 20180404 - DETECTING MALICIOUS SOFTWARE THROUGH CONTEXTUAL CONVICTIONS, GENERIC SIGNATURES AND MACHINE LEARNING TECHNIQUES
Title (en)
DETECTING MALICIOUS SOFTWARE THROUGH CONTEXTUAL CONVICTIONS, GENERIC SIGNATURES AND MACHINE LEARNING TECHNIQUES
Title (de)
ERKENNUNG SCHÄDLICHER SOFTWARE DURCH KONTEXTUELLE BEWERTUNGEN, GENERISCHE SIGNATUREN UND MASCHINENLEHRTECHNIKEN
Title (fr)
DÉTECTION D'UN LOGICIEL MALVEILLANT PAR LE BIAIS D'INFORMATIONS CONTEXTUELLES, DE SIGNATURES GÉNÉRIQUES ET DE TECHNIQUES D'APPRENTISSAGE MACHINE
Publication
Application
Priority
- US 41858010 P 20101201
- US 2011062957 W 20111201
Abstract (en)
[origin: WO2012075336A1] Novel methods, components, and systems that enhance traditional techniques for detecting malicious software are presented. More specifically, methods, components, and systems that use important contextual information from a client system (such as recent history of events on that system), machine learning techniques, the automated deployment of generic signatures, and combinations thereof, to detect malicious software. The disclosed invention provides a significant improvement with regard to automation compared to previous approaches.
IPC 8 full level
G06F 12/14 (2006.01); G06F 12/16 (2006.01); G06F 21/56 (2013.01)
CPC (source: EP)
G06F 21/56 (2013.01); H04L 51/212 (2022.05)
Designated contracting state (EPC)
AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR
DOCDB simple family (publication)
WO 2012075336 A1 20120607; AU 2011336466 A1 20130718; AU 2011336466 B2 20160825; AU 2011336466 C1 20170119; BR 112013013625 A2 20160913; CA 2819832 A1 20120607; CA 2819832 C 20170314; EP 2646911 A1 20131009; EP 2646911 A4 20140723; EP 2646911 B1 20180404; JP 2014504399 A 20140220; JP 5961183 B2 20160802
DOCDB simple family (application)
US 2011062957 W 20111201; AU 2011336466 A 20111201; BR 112013013625 A 20111201; CA 2819832 A 20111201; EP 11844348 A 20111201; JP 2013542187 A 20111201